Hypergraphs offer a generalized framework for capturing high-order relationships between entities and have been widely applied in various domains, including healthcare, social networks, and bioinformatics. Hypergraph neural networks, which rely on message-passing between nodes over hyperedges to learn latent representations, have emerged as the method of choice for predictive tasks in many of these domains. These approaches typically perform only a small number of message-passing rounds to learn the representations, which they then utilize for predictions. The small number of message-passing rounds comes at a cost, as the representations only capture local information and forego long-range high-order dependencies. However, as we demonstrate, blindly increasing the message-passing rounds to capture long-range dependency also degrades the performance of hyper-graph neural networks.
Recent works have demonstrated that implicit graph neural networks capture long-range dependencies in standard graphs while maintaining performance. Despite their popularity, prior work has not studied long-range dependency issues on hypergraph neural networks. Here, we first demonstrate that existing hypergraph neural networks lose predictive power when aggregating more information to capture long-range dependency. We then propose Implicit Hypergraph Neural Network (IHNN), a novel framework that jointly learns fixed-point representations for both nodes and hyperedges in an end-to-end manner to alleviate this issue. Leveraging implicit differentiation, we introduce a tractable projected gradient descent approach to train the model efficiently. Extensive experiments on real-world hypergraphs for node classification demonstrate that IHNN outperforms the closest prior works in most settings, establishing a new state-of-the-art in hypergraph learning.
Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
National Engineering Research Center for Big Data Technology and System（国家大数据技术与系统工程研究中心）；School of Computer Science and Technology（计算机科学与技术学院）；Huazhong University of Science and Technology（华中科技大学）
Towards Trustworthy Hypergraph Neural Networks under Label Noise
Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Mathematics and Statistics, Shandong University（山东大学数学与统计学院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger
Beijing University of Posts and Telecommunications（北京邮电大学）；North China Electric Power University（华北电力大学）；University of Kent（肯特大学）；Beihang University（北航）；University of Illinois（伊利诺伊大学）
Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation
School of Mathematics and Statistics, Shandong University, Weihai（山东大学（威海）数学与统计学院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；University of Chinese Academy of Sciences（中国科学院大学）
Counterfactual Explanations for Hypergraph Neural Networks
Department of Computer Control and Management Engineering, Sapienza University（计算机控制与管理工程系，萨皮恩扎大学）；Department of Computer Science, Sapienza University（计算机科学系，萨皮恩扎大学）
High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach
School of Artifcial Intelligence, Xidian University（西安电子科技大学人工智能学院）；School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）；Guangzhou Institute of Technology, Xidian University（西安电子科技大学广州研究院）
Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits
University of Southern California（南加州大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Texas Tech University（德克萨斯理工大学）；Southern Methodist University（南卫理公会大学）
HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness
FGeo-HyperGNet: Geometric Problem Solving Integrating FormalGeo Symbolic System and Hypergraph Neural Network
School of Computer Engineering and Science, Shanghai University（上海大学计算机工程与科学学院）；School of Future Technology, Shanghai University（上海大学未来技术学院）；College of Sciences, Shanghai University（上海大学理学院）
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；Ant Group（蚂蚁集团）；School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；School of Computer Science, Fudan University（复旦大学计算机学院）；School of Science, Computing and Engineering Technologies, Swinburne University of Technology（斯威本科技大学科学、计算与工程技术学院）
Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）
Stability and Generalization of Hypergraph Collaborative Networks
Institute of Data Science, The University of Hong Kong（香港大学数据科学学院）；College of Information Science and Technology, Jinan University（暨南大学信息科学技术学院）；The University of Hong Kong（香港大学）
HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems
School of Mathematical Sciences, University of Chinese Academy of Science（中国科学院大学数学科学学院）；Zhongguancun Laboratory（中关村实验室）；School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）
Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification
School of Computer Science and Technology, East China Normal University（计算机科学与技术学院，东华大学）；NPPA Key Laboratory of Publishing Integration Development, ECNUP（出版集成发展关键实验室，ECNUP）
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network
School of Computer Science, Hubei University（湖北大学计算机学院）；Hubei Key Laboratory of Big Data Intelligent Analysis and Application (Hubei University)（湖北大学大数据智能分析与应用重点实验室）；Discipline of Business Analytics, The University of Sydney（悉尼大学商业分析学科）；Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education（智能感知系统与安全重点实验室（湖北大学），教育部）
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Huawei Hisilicon（华为海思）；Peking University（北京大学）
School of Cyberspace, Hangzhou Dianzi University（杭州电子科技大学信息学院）；Data Security Governance Zhejiang Engineering Research Center（浙江数据安全治理工程研究中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学院）
UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification
Fudan University（复旦大学）；Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University（复旦大学工程与应用技术研究院人工智能与机器人研究所）；Research Institute of Intelligent Complex Systems, Fudan University（复旦大学智能复杂体系研究院）
